A simulation device and method are provided. The simulation device includes an interface, a storage, and a processor. The storage stores a plurality of digital twin models, each of the digital twin models simulates one entity of the at least one production line. The processor performs the following operations: generating a plurality of task requirements according to a production specification and a production data; broadcasting the task requirements to each of the digital twin models that meets one of the task requirements, wherein each of the digital twin models generates a state report based on the received task requirements; generating a plurality of task twin models based on the state reports, wherein each of the task twin models includes a group of digital twin models corresponding to the task requirements; and generating a plurality of simulation results according to the task twin models.
Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A simulation device, comprising: an interface, being configured to access a production specification and a production data related to at least one production line; a storage, storing a plurality of digital twin models, each of the digital twin models simulating one entity of the at least one production line; a processor, being electrically connected to the interface and the storage and configured to perform the following operations: generating a plurality of task requirements according to the production specification and the production data, wherein the task requirements indicate a plurality of production capacity needs to achieve the production specification; broadcasting the task requirements to each of the digital twin models that meets one of the task requirements, wherein each of the digital twin models generates a state report based on the received task requirements, and each of the state reports helps the processor to determine a production schedule corresponding to each of the task requirements; generating a plurality of task twin models based on the state reports, wherein each of the task twin models includes a group of digital twin models corresponding to the task requirements; and generating a plurality of simulation results according to the task twin models.
A simulation device is designed to optimize production line operations by leveraging digital twin models. The device addresses inefficiencies in production planning by dynamically simulating and scheduling tasks across multiple production entities. The system includes an interface to access production specifications and real-time production data from one or more production lines. A storage unit holds multiple digital twin models, each representing a distinct entity within the production line, such as machines, workstations, or processes. A processor generates task requirements based on the production specifications and data, defining the necessary production capacities to meet targets. These requirements are broadcast to relevant digital twin models, which respond with state reports indicating their operational status and capabilities. The processor uses these reports to determine optimal production schedules for each task. Task twin models are then created, grouping digital twin models that meet specific task requirements. The system simulates these task twin models to generate results, enabling data-driven decision-making for production planning and resource allocation. This approach enhances efficiency by dynamically adapting to production demands and constraints.
2. The simulation device of claim 1 , wherein each of the entities is one of an operator and a machine of the at least one production line.
This invention relates to a simulation device for optimizing production line operations. The device simulates interactions between entities within a production system, where each entity can be either an operator or a machine. The simulation models the behavior and performance of these entities to identify inefficiencies, bottlenecks, or other operational issues in the production process. By analyzing the simulated interactions, the device helps improve workflow, reduce downtime, and enhance overall productivity. The simulation may include variables such as processing times, resource allocation, and task sequencing to provide actionable insights for real-world production optimization. The device is particularly useful in manufacturing environments where precise coordination between human operators and automated machinery is critical. The simulation can be adjusted to test different configurations, schedules, or operational strategies before implementation, reducing the risk of disruptions in actual production. The goal is to create a more efficient, reliable, and cost-effective production system by leveraging predictive modeling and data-driven decision-making.
3. The simulation device of claim 2 , wherein when one entity simulated by one of the digital twin models is a first operator, the corresponding digital twin model includes at least one of work experience, work skill, and a piece of information of production rate of the first operator.
This invention relates to simulation devices that use digital twin models to represent entities in a system, such as operators in a production environment. The problem addressed is the need for accurate and detailed simulation of human operators, including their skills, experience, and productivity, to improve system performance and decision-making. The simulation device includes multiple digital twin models, each representing a different entity in the system. When one of these entities is a human operator, the corresponding digital twin model incorporates at least one of the operator's work experience, work skills, or production rate data. This allows the simulation to realistically model how the operator's characteristics influence system behavior. The digital twin models interact with each other and with the physical system to provide insights into performance, efficiency, and potential improvements. By integrating operator-specific data into the digital twin models, the simulation can more accurately predict outcomes, optimize workflows, and identify training needs. This approach enhances the reliability of simulations in manufacturing, logistics, and other industries where human performance is a critical factor. The system enables better decision-making by accounting for variations in operator capabilities and productivity.
4. The simulation device of claim 2 , wherein when one entity simulated by one of the digital twin models is a first machine, the corresponding digital twin model includes at least one of work content, an operation condition, and a piece of information of production rate of the first machine.
This invention relates to simulation devices that use digital twin models to represent physical entities in a system, such as machines in a production environment. The problem addressed is the need for accurate and dynamic simulation of machine behavior to optimize operations, predict performance, and improve decision-making in industrial settings. The simulation device includes digital twin models that replicate the behavior of physical entities, such as machines, in a simulated environment. Each digital twin model for a machine includes at least one of the following: work content, operation conditions, and production rate information. Work content refers to the tasks or processes the machine performs, while operation conditions describe the environmental or operational parameters affecting the machine, such as temperature, speed, or load. Production rate information provides data on the machine's output efficiency, such as units produced per hour. By incorporating these elements into the digital twin model, the simulation device can accurately simulate the machine's behavior under different scenarios, allowing for real-time monitoring, predictive maintenance, and process optimization. The inclusion of work content, operation conditions, and production rate data ensures that the simulation reflects real-world conditions, enabling more precise predictions and better-informed decisions. This approach enhances operational efficiency, reduces downtime, and improves overall system performance in industrial applications.
5. The simulation device of claim 1 , wherein each of the state reports includes at least one of an operation plan, a unit cost, a workload, and a production rate.
This invention relates to a simulation device for optimizing industrial or manufacturing processes by analyzing state reports generated during simulations. The device addresses the challenge of efficiently evaluating and improving production workflows by providing detailed insights into key performance metrics. The simulation device generates state reports that include at least one of an operation plan, unit cost, workload, or production rate. These reports allow users to assess different aspects of the production process, such as scheduling, cost efficiency, resource utilization, and output capacity. By incorporating these metrics, the device enables users to identify bottlenecks, optimize resource allocation, and enhance overall productivity. The simulation device may also include a user interface for visualizing the state reports, allowing for real-time adjustments and decision-making. The inclusion of multiple performance indicators ensures a comprehensive analysis, helping industries streamline operations and reduce inefficiencies. This approach is particularly useful in manufacturing, logistics, and other sectors where process optimization is critical. The device's ability to simulate and analyze various scenarios makes it a valuable tool for improving operational efficiency and cost-effectiveness.
6. The simulation device of claim 1 , wherein the processor further receives a target demand, and generates an operational suggestion according to the target demand and the simulation results.
This invention relates to a simulation device used in industrial or operational environments to optimize performance based on demand. The device includes a processor that simulates operational conditions of a system, such as a manufacturing plant or energy grid, to predict outcomes under various scenarios. The processor analyzes input parameters like resource availability, environmental factors, and system constraints to generate simulation results that forecast efficiency, output, or other performance metrics. The processor also receives a target demand, which represents a desired operational goal, such as maximizing production or minimizing energy consumption. Using the simulation results and the target demand, the processor generates an operational suggestion—a recommended action or adjustment to achieve the target. For example, the suggestion may involve adjusting production rates, reallocating resources, or modifying system settings to meet the demand while optimizing efficiency. The device helps operators make data-driven decisions by providing actionable insights derived from predictive simulations. This approach reduces trial-and-error adjustments and improves overall system performance.
7. The simulation device of claim 6 , wherein the target demand includes at least one of a minimal total cost configuration, a maximum demand fulfillment configuration, and a maximum profit configuration.
This invention relates to simulation devices used in supply chain or resource allocation systems, addressing the challenge of optimizing demand fulfillment under various constraints. The device simulates demand scenarios to determine optimal configurations for cost, demand fulfillment, or profit. It evaluates different configurations to identify the most efficient allocation of resources, whether minimizing total cost, maximizing demand fulfillment, or maximizing profit. The simulation device processes input data representing demand patterns, resource availability, and cost factors to generate output configurations that meet predefined objectives. By analyzing multiple configurations, the device helps decision-makers select the best strategy based on specific goals, such as cost efficiency, demand satisfaction, or revenue optimization. The system may integrate with existing supply chain or logistics platforms to provide real-time or predictive insights. The invention improves decision-making by quantifying trade-offs between cost, demand, and profit, enabling more informed resource allocation.
8. The simulation device of claim 1 , further comprising a display for displaying the simulation results through visualization.
This invention relates to simulation devices used in engineering, scientific research, or training applications. The primary problem addressed is the need for effective visualization of simulation results to enhance user understanding and decision-making. Traditional simulation systems often lack integrated display capabilities, requiring separate tools for data interpretation, which can be inefficient and error-prone. The simulation device includes a processing unit configured to execute simulations based on input parameters, generating results such as numerical data, graphical outputs, or predictive models. A key feature is the inclusion of a display specifically designed to visualize these results in real-time or post-simulation. The display may present data through charts, graphs, 3D models, or interactive interfaces, allowing users to analyze trends, identify anomalies, or validate outcomes. The visualization can be customized to highlight critical variables or scenarios, improving clarity and usability. Additionally, the device may incorporate user input mechanisms, such as controls or interfaces, to adjust simulation parameters dynamically. This enables iterative testing and refinement of models. The display can also support multi-modal outputs, including audio or haptic feedback, to enhance user engagement. By integrating visualization directly into the simulation device, the invention streamlines workflows, reduces reliance on external tools, and provides a more immersive and intuitive user experience. This is particularly valuable in fields like aerospace, automotive engineering, or medical training, where real-time feedback is crucial.
9. The simulation device of claim 1 , wherein each of the simulation results is a simulated production line schedule, and the simulated production line schedule is at least one production line configuration corresponding to the production specification.
This invention relates to a simulation device for optimizing production line configurations based on production specifications. The device addresses the challenge of efficiently determining optimal production line setups to meet specific manufacturing requirements while minimizing downtime and resource waste. The simulation device generates multiple simulated production line schedules, each representing a different production line configuration that aligns with the given production specifications. These configurations are evaluated to determine their effectiveness in meeting the specified production targets, such as output volume, quality standards, and resource utilization. The device simulates various scenarios to identify the most efficient production line setup, ensuring that the chosen configuration maximizes productivity while adhering to constraints like equipment availability, labor allocation, and material flow. By simulating different production line configurations, the device helps manufacturers avoid costly trial-and-error approaches in real-world production environments. The results provide actionable insights into the best possible production line arrangement, allowing for data-driven decision-making to enhance operational efficiency and reduce production bottlenecks. The invention is particularly useful in industries where production specifications frequently change, requiring rapid adaptation of production line setups to maintain optimal performance.
10. A simulation method, being adapted for a simulation device, the simulation device accessing a production specification and a production data related to at least one production line, storing a plurality of digital twin models, each of the digital twin models simulating one entity of the at least one production line, the simulation method being performed by the simulation device and comprising: generating a plurality of task requirements according to the production specification and the production data, wherein the task requirements indicate a plurality of production capacity needs to achieve the production specification; broadcasting the task requirements to each of the digital twin models that meets one of the task requirements, wherein each of the digital twin models generates a state report based on the received task requirements, and each of the state reports helps the simulation device to determine a production schedule corresponding to each of the task requirements; generating a plurality of task twin models based on the state reports, wherein each of the task twin models includes a group of digital twin models corresponding to the task requirements; and generating a plurality of simulation results according to the task twin models.
This invention relates to a simulation method for optimizing production line operations using digital twin models. The method addresses the challenge of efficiently allocating production resources to meet specified production targets while accounting for real-time production data and constraints. The simulation device accesses a production specification and production data related to one or more production lines. It stores multiple digital twin models, each simulating a specific entity within the production line, such as machinery, workstations, or processes. The method generates task requirements based on the production specification and production data, which define the production capacity needs required to meet the specified targets. These task requirements are broadcast to relevant digital twin models that can fulfill them. Each digital twin model generates a state report based on the received task requirements, allowing the simulation device to determine an optimal production schedule for each task. The method then creates task twin models, which are groups of digital twin models assigned to specific task requirements. Finally, the simulation device generates simulation results based on these task twin models, providing insights into production efficiency, resource allocation, and potential bottlenecks. This approach enables dynamic and adaptive production planning by leveraging digital twin technology to simulate and optimize production line performance under varying conditions.
11. The simulation method of claim 10 , wherein each of the entities is one of an operator and a machine of the at least one production line.
This invention relates to a simulation method for optimizing production line operations. The method addresses the challenge of efficiently modeling and analyzing production processes involving multiple entities, such as operators and machines, to improve workflow efficiency and resource allocation. The simulation method involves creating a digital model of a production line, where each entity—whether an operator or a machine—is represented as an independent agent within the system. These entities interact according to predefined rules that mimic real-world operational constraints, such as machine downtime, operator availability, and task dependencies. The simulation dynamically adjusts these interactions to evaluate different production scenarios, such as changes in workflow, equipment maintenance schedules, or operator assignments. By simulating these scenarios, the method identifies bottlenecks, inefficiencies, or optimal configurations that enhance productivity. The system may also incorporate real-time data from actual production lines to refine the simulation accuracy. The goal is to provide actionable insights for improving operational efficiency, reducing downtime, and optimizing resource utilization in manufacturing environments. The method is particularly useful in industries where production lines involve complex interactions between human operators and automated machinery.
12. The simulation method of claim 11 , wherein when one entity simulated by one of the digital twin models is a first operator, the corresponding digital twin model includes at least one of work experience, work skill, and a piece of information of production rate of the first operator.
This invention relates to digital twin simulation methods for modeling and simulating human operators in industrial or production environments. The problem addressed is the lack of accurate representation of human factors in digital twin simulations, which can lead to inefficiencies in process optimization, training, and decision-making. The method involves creating digital twin models that simulate entities within a system, with a focus on human operators. For a digital twin representing an operator, the model incorporates at least one of the operator's work experience, work skills, or production rate data. This allows the simulation to realistically replicate how an operator's individual characteristics influence performance. The digital twin models interact with other simulated entities, such as machinery or environmental factors, to provide a comprehensive simulation of the production process. By integrating operator-specific data, the simulation can predict how different operators may affect productivity, identify training needs, and optimize workflows. The method improves upon prior art by accounting for human variability, leading to more accurate simulations and better-informed decisions in industrial settings. The approach is applicable in manufacturing, logistics, and other domains where human performance impacts system efficiency.
13. The simulation method of claim 11 , wherein when one entity simulated by one of the digital twin models is a first machine, the corresponding digital twin model includes at least one of work content, an operation condition, and a piece of information of production rate of the first machine.
This invention relates to digital twin simulation methods for industrial systems, particularly for optimizing machine operations and production processes. The method involves creating digital twin models of physical entities, such as machines, to simulate their behavior and interactions within a production environment. The digital twin models incorporate key operational data, including work content, operation conditions, and production rate information, to accurately represent the performance of physical machines. By simulating these models, the system can predict outcomes, identify inefficiencies, and optimize production workflows. The method allows for real-time adjustments based on simulated data, improving overall system performance and reducing downtime. The invention addresses the challenge of efficiently managing complex industrial systems by leveraging digital twin technology to enhance decision-making and operational accuracy. The simulation method dynamically updates the digital twin models to reflect changes in the physical entities, ensuring continuous alignment between the virtual and real-world systems. This approach enables proactive maintenance, better resource allocation, and improved production planning. The invention is particularly useful in manufacturing, where precise control over machine operations is critical for maintaining productivity and quality.
14. The simulation method of claim 10 , wherein each of the state reports includes at least one of an operation plan, a unit cost, a workload, and a production rate.
This invention relates to simulation methods for optimizing industrial or manufacturing processes. The method addresses the challenge of efficiently modeling and analyzing production systems to improve decision-making, cost management, and operational efficiency. The simulation method generates state reports that provide detailed insights into various aspects of the production process. Each state report includes at least one of an operation plan, unit cost, workload, or production rate. These reports enable users to assess different performance metrics, identify bottlenecks, and optimize resource allocation. The operation plan outlines scheduled tasks and workflows, while the unit cost provides financial data per unit produced. Workload metrics track resource utilization, and production rate data measures output efficiency. By integrating these elements, the method supports data-driven adjustments to enhance productivity and reduce costs. The simulation method is particularly useful in manufacturing, logistics, and supply chain management, where real-time or predictive analytics are critical for maintaining operational efficiency. The inclusion of multiple performance indicators in state reports allows for comprehensive analysis and informed decision-making.
15. The simulation method of claim 10 , further comprising: receiving a target demand; and generating an operational suggestion according to the target demand and the simulation results.
This invention relates to simulation methods for optimizing operational decisions in industrial or manufacturing systems. The core problem addressed is the need to efficiently generate actionable recommendations based on simulated operational data to meet specific demand targets. The method involves simulating the operation of a system, such as a manufacturing plant or supply chain, to predict performance under various conditions. This includes modeling resource allocation, production rates, and other operational parameters to generate simulation results. The simulation accounts for constraints like resource availability, time limits, and cost factors to ensure realistic outcomes. Additionally, the method receives a target demand, which could be a production goal, inventory level, or other performance metric. Using the simulation results, the system generates an operational suggestion tailored to meet this target. The suggestion may include adjustments to production schedules, resource allocation, or other operational changes to optimize efficiency and performance. The simulation may incorporate historical data, real-time inputs, or predictive models to refine accuracy. The operational suggestions are designed to balance competing priorities, such as minimizing costs while maximizing output or meeting deadlines. This approach helps decision-makers optimize operations dynamically in response to changing demands or constraints.
16. The simulation method of claim 15 , wherein the target demand includes at least one of a minimal total cost configuration, a maximum demand fulfillment configuration, and a maximum profit configuration.
This invention relates to simulation methods for optimizing demand fulfillment in supply chain or resource allocation systems. The method addresses the challenge of efficiently balancing cost, demand fulfillment, and profitability when allocating resources or configuring systems to meet varying demands. The core technique involves simulating different demand scenarios and evaluating configurations based on predefined objectives. The method first generates a target demand profile, which represents the expected requirements or constraints for a given scenario. This demand profile is then used to simulate the performance of different configurations. The configurations are evaluated based on at least one of three key objectives: minimizing total cost, maximizing demand fulfillment, or maximizing profit. The simulation compares these configurations to determine the optimal or most efficient solution for the given demand. The method may also incorporate additional constraints or variables, such as resource availability, operational limits, or external factors, to refine the simulation results. By systematically testing different configurations against the target demand, the method helps identify the most effective allocation strategy. This approach is particularly useful in industries like logistics, manufacturing, or energy management, where optimizing resource use under varying demand conditions is critical. The simulation provides actionable insights for decision-making, ensuring that configurations align with business or operational goals.
17. The simulation method of claim 10 , wherein the simulation device further comprises a display and the simulation method further comprises: displaying, by the display, the simulation results through visualization.
This invention relates to a simulation method for visualizing simulation results, particularly in fields such as engineering, scientific research, or data analysis where understanding complex data through visualization is critical. The method addresses the challenge of effectively presenting simulation outcomes to users, enabling better interpretation and decision-making. The simulation method involves using a simulation device equipped with a display to generate and process simulation data. The device executes a simulation process, which may include modeling physical systems, running computational algorithms, or analyzing datasets. The simulation results, which could be numerical, graphical, or multi-dimensional, are then visualized on the display. This visualization may include graphs, charts, 3D models, or interactive interfaces that allow users to explore the data dynamically. The display provides real-time or post-processing visualization, enhancing user comprehension of the simulation outcomes. The method ensures that complex data is presented in an intuitive and accessible format, improving usability and applicability in various technical and scientific domains.
18. The simulation method of claim 10 , wherein each of the simulation results is a simulated production line schedule, and the simulated production line schedule is at least one production line configuration corresponding to the production specification.
This invention relates to simulation methods for optimizing production line schedules in manufacturing environments. The problem addressed is the need to efficiently generate and evaluate multiple production line configurations to meet specific production specifications while minimizing downtime and maximizing throughput. The method involves simulating production line schedules by generating multiple configurations that align with given production specifications. Each simulated schedule represents a potential arrangement of production line components, such as machines, workflows, and resource allocations, to fulfill manufacturing requirements. The simulation evaluates these configurations to determine the most efficient setup, considering factors like production capacity, resource availability, and operational constraints. The method further includes analyzing the simulation results to identify optimal configurations that meet or exceed the production specifications. This involves comparing simulated schedules based on performance metrics such as cycle time, resource utilization, and cost efficiency. The goal is to select the best-performing configuration for real-world implementation, ensuring that the production line operates at peak efficiency while adhering to the specified requirements. By automating the simulation and evaluation process, the method reduces the time and effort required to plan and optimize production line setups, leading to improved manufacturing productivity and reduced operational costs.
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January 31, 2019
April 12, 2022
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